Conversational Artificial Intelligence (AI) has transformed human–computer interaction by enabling intelligent, context-aware, and natural communication. However, most chatbot systems rely on Large Language Models (LLMs), which demand significant computational resources, memory, and cloud infrastructure. This paper presents an Interactive AI Conversation System based on a Small Language Model (SLM) chatbot that provides efficient, low-latency, and cost-effective conversational capabilities. The proposed system employs natural language processing (NLP), transformer-based SLM architecture, intent recognition, contextual memory, and response generation to deliver interactive conversations while operating with significantly lower computational requirements. The chatbot is designed for educational assistance, customer support, healthcare guidance, and enterprise applications. Experimental evaluation demonstrates reduced inference latency, lower memory consumption, and competitive conversational quality, making the proposed system suitable for deployment on edge devices and resource-constrained environments. Small Language Models are increasingly attractive because they provide lower latency and lower resource usage while remaining effective for many specialized conversational tasks.
Priya et al. (Mon,) studied this question.
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